The present invention relates generally to neural network systems and methods and, more particularly, to a position and orientation estimation neural network system and processing method that may be employed in an automatic target recognition system.
There are several conventional approaches that have been used to estimate the position and orientation of objects in computer vision applications, and particularly in automatic target recognition applications. The gradient descent approach is commonly used to solve object recognition problems of this type. However the gradient descent approach has the problem of getting stuck in "local minima" and thus produces poor solutions. The gradient descent and exhaustive search approaches are also computationally inefficient, and are difficult to implement on a real time hardware system. Furthermore, the conventional approaches do not provide practical solutions within high-dimensional problem spaces.
Accordingly, it is an objective of the present invention to provide a position and orientation estimation neural network system that overcomes the limitations of conventional approaches. It is also an objective of the present invention to provide a position and orientation estimation neural network system that may be employed in an automatic target recognition system.